from torch.optim.lr_scheduler import PolynomialLRscheduler = PolynomialLR(optimizer, total_iters = 8, # The number of steps that the scheduler decays the learning rate. power = 1) # The power of the polynomial.下图为power= 1时的学习率衰减结果。power= 1时，学习率衰减如下所示。7、Cos...
gamma =0.5) # Multiplicative factor of learning rate decay 3、ConstantLR ConstantLR通过乘法因子降低学习率,直到训练达到预定义步数。 from torch.optim.lr_scheduler import ConstantLR scheduler = ConstantLR(optimizer, factor = 0.5, # The number we multiply learning rate until the milestone. total_iters...
fromtorch.optim.lr_schedulerimportStepLR# torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=- 1, verbose=False)deftrain_step():passbegin_epoch=0max_epoch=200# 从 0 到 200 epoch# 绘制 200 轮的学习率曲线defshow_lr(begin_epoch,max_epoch,scheduler):lr=[]...
from torch.optim.lr_scheduler import CyclicLR scheduler = CyclicLR(optimizer, base_lr = 0.0001, # Initial learning rate which is the lower boundary in the cycle for each parameter group max_lr = 1e-3, # Upper learning rate boundaries in the cycle for each parameter group step_size_up =...
scheduler= optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10) epochs= 30lr_list=list()foriinrange(epochs):print(scheduler.get_last_lr()) lr_list.append(scheduler.get_last_lr()) scheduler.step() _, ax=plt.subplots() ax.set_title('learning rate curve') ...
5. **学习率查找器(Learning Rate Finder)**: 在训练开始前,快速地遍历不同的学习率,以找到最佳的学习率范围。 ```python from torch.optim.lr_schedulerimport_LRScheduler class LRFinder(_LRScheduler): def __init__(self, optimizer,end_lr=10,num_it=100,last_epoch=-1): ...
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) #随机梯度下降算法 1. 2. 定义训练函数: 在单个训练循环中,模型对训练数据集进行预测(分批输入),并将预测误差反向传播以调整模型参数。 训练函数传入四个参数:数据、网络模型、损失函数、优化函数。
classCustomMultiplicativeLR(CustomLambdaLR):def__init__(self,optimizer,lr_lambda,last_epoch=-1):super(CustomMultiplicativeLR,self).__init__(optimizer,lr_lambda,last_epoch)defget_lr(self):ifself.last_epoch>0:return[group['lr']*lmbda(self.last_epoch)forlmbda,groupinzip(self.lr_lambdas,self...
参考:https://pytorch.org/docs/master/optim.html#how-to-adjust-learning-rate torch.optim.lr_scheduler提供了几种方法来根据迭代的数量来调整学习率 自己手动定义一个学习率衰减函数: def adjust_learning_rate(optimizer, epoch, lr):"""Sets the learning rate to the initial LR decayed by 10 every 2 ...
) for images, target in tqdm(train_dataloader): images, target = images.to(device), target.to(device) images.requires_grad=True optimizer.zero_grad() #Applying gradient checkpointing segments = 2 # get the modules in the model. These modules should be in the order ...